The present invention relates generally to the use of a computerized dosimetry system for planning the spatial configuration of radioactive seeds and the associated dose intensity distribution inside and surrounding a planning target volume in the irradiation of cancerous tissue.
More particularly, the present invention relates to a method of and a system for using artificial intelligence to determine the most appropriate seed configuration for a given prostate shape and size, with minimal human intervention.
In the treatment of prostate cancer, a method is often employed to implant numerous radioactive seeds in a carefully preplanned pattern in three dimensions within the prostate. That procedure serves to deliver a known amount of radiation dosage concentrated around the prostate, while at the same time sparing radiation-sensitive tissues, such as the urethra, the bladder and the rectum. Customarily, 60 to 120 seeds are placed through 15 to 30 needles in the inferior (feet) to superior (head) direction (see FIG. 1). These needle positions are selected from a 13.times.13 grid of 0.5 cm evenly spaced template holes, which are used to achieve precise needle insertion. The number of these holes that intersect with the prostate cross section, and therefore are potentially usable, is typically about 60 (see FIG. 2). The number of mathematical combinations is therefore greatly in excess of 10.sup.16, each of which is a potential treatment plan but is associated with different degrees of cancer control and the likelihood of treatment complications.
In current clinical practice, the design of a suitable seed configuration which is customized to the anatomy of a patient is achieved by a highly trained medical physicist or dosimetrist only by using trial-and-error manual iterations. The practitioner usually starts with an initial needle configuration based on experience or rules of thumb, and then adjusts the radioactive strength per seed or the locations of certain needles or both, until the calculated dose intensity distribution satisfies a set of clinical considerations. That process requires between 15 minutes and 2 hours, depending on the experience of the treatment planner and the geometric complexity of the relationship between the prostate and the surrounding anatomic structures.
These known treatment planning processes are typically aided by one of several available commercial computerized treatment planning systems. Such treatment planning systems enable the user to outline the prostate in relation to a template grid, to turn on or off any available needle positions and seed positions within each needle, and to examine the resultant dose distribution in two or three dimensions. Examples of such planning systems include those offered by Multimedia Medical Systems (MMS) of Charlottesville, Va., SSGI Prowess, of Chico, Calif., Nucletron Plato, from Columbia, Md., Computerized Medical Systems (CMS) Focus, of St Louis, Mo., Radiation Oncology Computer Systems (ROCS), of Carlsbad, Calif., ADAC Laboratory's Pinnacle, of Milpitas, Calif. and Theraplan, available from Theratronics International Ltd. of Kanata, Ontario, Canada.
In a number of such known commercial treatment planning systems, for example, those available from MMS and SSGI, the initial needle configuration that otherwise would have to be turned on by the human treatment planner is automatically set up by the computer system. That initial setup is based on simple rules of thumb, such as uniform loading, peripheral loading or modified peripheral loading. In a number of instances, the manufacturer claims that its planning system offers "automatic planning", "geometric optimization", or "real-time dosimetry". However, none of these commercial planning systems offer true optimization in that the automatically loaded seeds are not designed based on customized dosimetric calculations. Rather, they are designed to fill the space of the prostate in some predetermined manner. Therefore, such known automatic seed loading techniques are designed to save between 15 to 30 mouse clicks by the operator (or about 1 minute of operation). However, the user is still required to apply his or her expert knowledge to iteratively improve upon this initial design in order to achieve customized planning for any individual patient. Thus, there are two significant drawbacks of the above-mentioned current techniques: First, the complete treatment planning process is under the manual guidance of a radiation planning expert using trial and error techniques; and second, the adjustment of the delivered dose is achieved by varying the radioactive strength per seed until an isodose surface with the desired shape and size is scaled up or down to the prescription dose, i.e., these techniques will suffer when the activity per seed is fixed, as at the time of surgical implantation in the operating suite.
Because of these two severe drawbacks, the currently available commercial treatment planning systems are not suitable for intraoperative treatment planning in the surgical suite, where the patient is placed under anesthesia in volatile conditions and where the cost per minute is very high. The variability of human performance, experience and stress, and the general inability of humans to manage large amounts of numerical data in 1 to 2 minutes are also factors that deter current practitioners from performing intraoperative treatment planning.
Although not designed for the express purpose of intraoperative optimized treatment planning, four previously published articles have described methods to automate the dosimetric planning (rather than simple geometric planning) for prostate seed implant brachytherapy. The references, features and deficiencies of these methods are as follows:
1. Roy J N; Wallner K E; Chiu-Tsao S T; Anderson L L; Ling C C. ["CT-based optimized planning for transperineal prostate implant with customized template." International Journal of Radiation Oncology Biology and Physics, 21:483-9 1991] This is an early attempt at computerized optimization and automation of dosimetric planning. The authors start from a manual design of the needle configuration, and use a least square computer algorithm to find the best seed loading pattern within the needles. The major limitation of this approach is that once the needle pattern is fixed by the human planner, only superficial degrees of freedom exist for the computer algorithm to optimize the dosimetry. For example, only the seed spacing within each needle can be varied, which is inconsistent with the standard technique of 1 cm uniform spacing. In addition, the least square optimization method is known to be unable to search widely for the best overall treatment plan in this multi-modal problem; it tends to settle for the nearest local optimal solution because there is no mechanism to test other design patterns which initially may be suboptimal. In general, any optimization method that presumes an existing fixed needle configuration (either designed manually by the dosimetric planner or automatically loaded based on geometric rules) does not allow sufficient variation in the possibilities of dose distribution to produce the optimal treatment plan.
2. Yu Y; Schell M C. ["A genetic algorithm for the optimization of prostate implants." Medical Physics, 23:2085-91 1996] This is the first attempt in using an "intelligent" computer algorithm, viz., the genetic algorithm, to explore the world of dosimetric possibilities for prostate brachytherapy planning. It is a theoretical work, not directly applicable to real-life prostate shapes and sizes. In fact, the prostate is schematically represented by ellipsoids of various sizes and elongations. The genetic algorithm is of an off-the-shelf generic kind, where the template is linearized into a string of bits, and not encoded by two dimensional genetic templates such as in the present invention. The article describes the use of a utility function for dosimetric comparison of competing treatment plans, which later turns out to be applicable only to one type of isotope and only to the prostate. In contrast, the present invention uses a multi-objective decision process to compare treatment plans in different aspects, such as dose to the prostate, urethra, rectum, and the sensitivity of the dose to surgical seed placement uncertainties. For these reasons, the method described in this article cannot be used under real-life clinical conditions.
3. Pouliot J; Tremblay D; Roy J; Filice S. ["Optimization of permanent .sup.125 I prostate implants using fast simulated annealing." International Journal of Radiation Oncology Biology and Physics, 36:711-20 1996] These authors apply simulated annealing, an optimization method commonly said to be inferior to genetic algorithms, to optimize prostate seed implant treatment plans. Their method is slow, requiring 15 minutes of run time on a workstation and tens of thousands of iterations to converge. In its scheme for comparing different treatment plans for quality, they assign a simple weighting factor to each of the terms in a cost function to represent its relative importance. In contrast, the present invention employs multi-objective optimization incorporating goals and satisfying constraints to produce the optimal plan in 1 to 2 minutes.
4. Chen Y, Stanton R E, Holst R J, Koprowski C D, Krisch E B. ["Treatment planning for prostate implant with loose seeds," Medical Physics 24:1141-1145 1997.] These authors use an ad hoc method to add one seed at a time into a prostate implant plan, until a single parameter that measures an aspect of the dosimetric quality approaches a broad minimum, signifying convergence. The method achieves automation by successively putting seeds into the location of the lowest dose inside the prostate. While this ad hoc method appears rational, it in no way performs optimization because the point of lowest dose at any instance during the progression of planning is not necessarily the final region of low dose, by virtue of the summation of doses from 60 to 120 other seeds.
In the broad area of genetic algorithms and optimization methodology, there have been a large number of articles which have described general and/or specific problems and solutions either in abstract or in concrete examples. Some of the well-known writings include those by Holland [Holland J H, Genetic algorithms, Scientific American 267:66-72, 1992] and by Goldberg [Goldberg D E, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, Ma., 1989)]. Even multi-objective optimization in the context of genetic algorithms have been described. Among these is an article written by the present inventor [Yu Y, "Multiobjective decision theory for computational optimization in radiation therapy," Medical Physics 24:1445-1454, 1997], using idealized prostate seed implants and idealized stereotactic radiosurgery as test cases. However, none of these prior methods address the unique two-dimensional nature of the template and the three-dimensional nature of the seed distribution, which bear important consequences to the optimization speed for prostate brachytherapy planning and thus its usability in the surgical suite. In addition, none of these prior methods use the accelerated n-tournament selection as described in the present invention, which also contributes to the speed of the present optimization system.
It is also important to have the capability of navigating the implantation scene on a three-dimensional basis. To that end, the prior art systems described above, such as that available from MMS and indeed all other commercial systems, use common 3D computer graphics for visualizing the prostate and the seed trains, as well as for inspecting the isodose surface. On the other hand, the present invention makes use of the Virtual Reality Modeling Language (VRML) to build an interactive navigational scene of surgery whose function is to guide needle insertions in real-time, to interrogate the anatomy of the patient, to alter the display state of the needles from unimplanted (emissive in color), to being implanted (pulsating colors), to implanted (dull color), and to serve as a verification tool at postoperative fluoroscopic imaging of the implant.
The standard procedure of a prostate seed implant before the advent of the instant Prostate Implant Planning Engine for Radiotherapy (PIPER) system of the present invention will now be described with reference to FIG. 3A. Having chosen a prostate implant as the treatment option of choice, the patient is subjected to a transrectal ultrasound (TRUS) volume study. That enables the treatment planning team (1) to determine the cross-sectional shape of the prostate on consecutive slices at 5 mm intervals from cranial (head) to caudal (feet) directions; (2) to overlay those prostate contours onto a template pattern that mimics the transperineal template used to guide the implantation; and (3) to get a measure of the total volume of the prostate. Often, the patient also undergoes a CT examination to make sure that the pubic bones do not interfere with the paths of the needle positions that must be chosen in the treatment plan.
The medical physics staff then takes the TRUS volume study information, inputs it into the treatment planning computer, and develops an acceptable treatment plan through a trial-and-error process that improves upon a set of treatment objectives. This treatment planning step allows the number of seeds and the radioactivity strength per seed to be determined, at which time the seeds can be ordered for this patient. A typical plan calls for 60-120 seeds. When the seeds arrive, which currently takes about 3 months, the medical physics staff performs a calibration check on the seed batch.
On the day of implantation, the patient is anesthetized and positioned on the operating table in a position which as closely as possible matches the position of the prior TRUS volume study. That position is verified under TRUS by viewing the same cross-sectional images of the prostate as in the volume study, in relation to the actual implantation template. The common difficulties with that approach are (1) the prostate often has changed size and/or shape; and (2) the prostate cannot be put in the same position with respect to the template as in the treatment plan. Those problems are now widely recognized by brachytherapy practitioners. It is generally acknowledged (for example, by the American Association of Physicists in Medicine Task Group No. 64 on Prostate Seed Implant Brachytherapy) that intraoperative computerized treatment planning is the only solution that overcomes these problems.
Ideally, when the prostate position on the template matches that of the preoperative plan, the implant proceeds exactly as prescribed by the plan. When all the seeds are placed, an x-ray image is taken in the operating room to confirm that the seed pattern is in reasonable agreement with expectations. Approximately 1 month thereafter, the patient returns for a CT examination of the implant. The CT information is transferred to the treatment planning system for postimplant dosimetry.
While the human treatment planning described above typically takes 2 hours per patient, the PIPER system of the present invention automatically generates optimized treatment plans customized to the patient in 2 minutes or less (based on 333 MHZ Pentium II PC speed). In addition to achieving improvements in the treatment plan quality as well as savings in human effort, the speed of the PIPER system of the present invention allows multiple patient management steps to be consolidated (see FIG. 3B). Thus, a batch of seeds can be periodically ordered for a stream of patients; eligible patients can be scheduled for implantation without delays due to preoperative planning and seed ordering; patient setup, TRUS volume study and treatment planning are combined to a single step, referred to as intraoperative planning, all performed in the operating room minutes before seed placement; matching of prostate position is no longer needed; and changes in prostate size and shape between planning and implantation are avoided.
In effect, the PIPER system of the present invention achieves a what-you-see-is-what-you-get (WYSIWYG) environment in the operating room. Still another added benefit is that since seeds are ordered in a batch for many patients, the safety margin (around 10-15%) of extra seeds previously ordered for each patient can now be shared, leading to a reduction in the number of wasted seeds (at $30-40 per seed), and thus a reduced cost.
After conducting a clinical study on ten consecutive implant patients on intraoperative optimized planning using the PIPER system of the present invention, the inventor has reached the following conclusions regarding the advantages of the present invention:
Intraoperative planning using the PIPER system of the present invention produces treatment plans of comparable quality to those from preoperative planning using the PIPER system of the present invention (which was shown in two previous studies to be superior to other planning techniques); PA1 Intraoperative planning is logistically feasible; PA1 Intraoperative planning significantly reduces ad hoc changes necessitated by prostate volume change. Using the known conventional technique, an average of 68% of the planned seed positions had to be modified. Under intraoperative planning with the PIPER system of the present invention, only 7% of the planned seed positions received minor modifications. PA1 Because of less changes, the overall time in the operating room as recorded by the anesthesiologist was shorter by 11% using the PIPER system of the present invention. That is a significant cost saving since the OR time is usually charged by the minute. PA1 Most of the participating clinicians scored their experience using the PIPER system of the present invention as better than their experience with the conventional preoperative experience.
The technology of the PIPER system of the present invention is built upon a synergistic formulation of a genetic algorithm, multi-objective decision theory and a statistical sensitive analysis.
The genetic algorithm is a method of intelligent computation, which has been applied in areas of artificial intelligence, engineering, defense, business and finance. The implementation of the genetic algorithm for brachytherapy is a unique aspect of the system of the present invention. It is the only optimization methodology presently known to have a demonstrated effectiveness for prostate seed implants. It uses a cooperative-competitive environment (coopetition) for potentially desirable treatment plans to most efficiently evolve to a single dominant pattern. Among the evolution processes implemented by the system of the present invention are sexual reproduction from parent patterns, mutation of the offspring, and competitive repopulation.
The multi-objective decision theory and sensitivity analysis used by the system of the present invention were formulated based on simulated annealing and stochastic computation. When coupled to the genetic algorithm, the multi-objective decision module ensures that the computer system's candidates for the optimal treatment plan are consistent with the clinicians' preferences and judgment. The sensitivity analysis module of the system of the present invention ensures that the chosen treatment plan is optimal, not only in the ideal configuration, but also after surgical uncertainties occur. The interplay of these three components of the PIPER system of the present invention provides a total solution for real-time intraoperative planning, where traditional manual planning under severe time-constraints and human pressure is simply not feasible.